Validation of HIV Risk Screening Tool to Identify Infected Adults and Adolescents >14 years at Community Level

Validation of HIV Risk Screening Tool to Identify Infected Adults and Adolescents >14 years at Community Level | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of HIV Risk Screening Tool to Identify Infected Adults and Adolescents >14 years at Community Level Kesetebirhan Delele Yirdaw, Justin Mandala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-209246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There are a number of risk factors being used to identify undiagnosed HIV infected adults. As the number of undiagnosed people gets lesser and lesser, it is important to know if existing risk factors and risk assessment tools are valid for use. In this study, we validate existing HIV risk assessment tools and see if they are worth using for HIV case finding among adults who remain undiagnosed. Methods The Tanzania and Zambia Population-Based HIV Impact Assessment (PHIA) household surveys were conducted during 2016. We used adult interview and HIV datasets to assess validity of different HIV risk assessment tools. We first included 12 risk factors (being divorced, separated or widowed (DSW); having an HIV+ spouse; having one of the following within 12 months of the survey: paid work, slept away from home for at least a month, had multiple sexual partners, paid for sex, had sexually transmitted infection (STI), being a tuberculosis (TB) suspect, being very sick for at least 3 months; had ever sold sex; diagnosed with cervical cancer; and had TB disease into a risk assessment tool and assessed its validity by comparing it against HIV test result. Sensitivity, specificity and predictive value of the tool were assessed against the HIV test result. A receiver operator characteristic (ROC) analysis was conducted to determine a suitable cut-off score in order to have a tool with better sensitivity, specificity, and PPV. ROC comparison statistics was used to statistically test equality between AUC (area under the curve) of the different scores. ROC comparison statistics was also used to determine which risk assessment tool was better compared to the tool that contained all risk factors. Results Of 14,820 study participants, 57.8% were men, and had a median age of 30 (IQR: 21-24). HIV prevalence was 2.3% (95% confidence interval (CI): 2.0-2.6). For the tool containing all risk factors, HIV prevalence was 1.0% when none of the risk factors were positive (Score 0) compared to 3.2% when at least one factor (Score ≥1) was present and 8.0% when ≥4 risk factors were present. Sensitivity, specificity, PPV, and NPV were 82.3% (78.6%-85.9%), 41.9% (41.1%-42.7%), 3.2% (2.8%-3.6%), and 99.0% (98.8%-99.3%), respectively. The use of a tool containing conventional risk factors (all except those related with working and sleeping away) was found to have higher AUC compared to the use of all risk factors (p value <0.001), with corresponding sensitivity, specificity, PPV, and NPV of 63.5% (58.9%-68.1%), 66.2% (65.5%-67.0%), 4.2% (3.6%-4.8%), and 98.7% (98.5%-98.9%), respectively. Conclusion Use of a screening tool containing conventional risk factors improved HIV testing yield compared to doing universal testing. Prioritizing people who fulfil multiple risk factors should be explored further to improve HIV testing yield. Health Economics & Outcomes Research Health Policy Infectious Diseases Adult HIV risk assessment tool Undiagnosed HIV Never tested for HIV HIV testing yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction HIV testing is the gate way for case finding, care and treatment as well as prevention services for high risk individuals. [ 1 – 3 ] Over the years remarkable progress has been made to diagnose infected people and put them on treatment. To date, Eastern and Southern African countries have coverage of 87% (77%-95%) for the first 90 while the coverage is 68% (54%-87%) for Central and Western African countries. [ 4 ] This correlates with high uptake of HIV testing across these countries. Prior HIV testing among surveyed men and women 15–49 years was 62% and 74% for Eastern, Southern and Central African countries, respectively from 2015–2018. It was much lower for Western African countries at 31% for women and 16% for men. [ 5 , 6 ] Maintaining such high testing coverage or conducting door-to-door testing in high risk communities is not feasible because of the limitation of funding available for HIV programs considering flattening of global support for HIV programs especially that of PEPFAR over the past 10 years. [ 7 , 8 ] As a result of that, a strategic shift has been made to implement targeted HIV testing with the aim of getting high testing yield per dollar spent on HIV test kit in many country HIV programs including high burden countries with the aim of putting as many infected people on treatment and reducing new infection and mortality in the process. [ 9 ] A number of HIV risk factors have been identified and in use to effect targeted HIV testing of at risk people. The World Health Organization (WHO) recommends HIV testing for clients having sexually transmitted infection (STI), viral hepatitis, tuberculosis (TB); key populations including commercial sex workers, men having sex with men, and IV drug users; clients with symptoms or medical conditions that could indicate HIV infection, including presumed and confirmed TB cases. [ 1 , 2 ] Other risk factors known to increase risk of HIV infection include having multiple sexual partners [ 10 , 11 ], being divorced, separated or widowed (DSW) [ 12 ], history of being a client of a sex worker [ 13 ], having cervical Ca [ 14 ], being partners with known infected person [ 15 ]. A number of HIV risk assessment tools were validated in different settings in an effort to determine best options to identify HIV infected adults. [ 10 , 16 , 17 ] These tools often don’t include risk factors recommended by the WHO and in use in high prevalence countries. Knowing the performance and limitation of a risk screening tool containing all common HIV risk factors is crucial to determine case finding strategies that better fit routine implementation setting and assess quality of testing services both in clinical and community settings. This study aims to: determine the performance of a hypothetical HIV risk assessment tool that contains conventional HIV risk factors to identify undiagnosed HIV + adults and adolescents > 14 years, determine the performance of a hypothetical HIV risk assessment tool that contains all potential HIV risk factors to identify undiagnosed HIV + adults and adolescents > 14 years, determine the performance of a hypothetical HIV risk assessment tool that contains only statistically significant HIV risk factors to identify undiagnosed HIV positive adults and adolescents > 14 years determine which of the above three tools is better in terms of overall performance to identify undiagnosed HIV positive adults and adolescents > 14 years determine if the presence of multiple HIV risk factors in one person improves performance of risk assessment tool to identify undiagnosed HIV positive adults and adolescents > 14 years. Materials And Methods Study setting and design This is a cross sectional study based on secondary analysis of data from two community based household surveys that were conducted in Zambia (2016) and Tanzania (2016–2017). These surveys were Population-Based HIV Impact Assessment (PHIA) studies conducted with PEPFAR support. [ 18 , 19 ] Study period The surveys were conducted during 2016–2017. Inclusion criteria adolescents and adults > 14 years who have matching interview and biomarker datasets (HIV testing result) and who had never tested for HIV prior the survey were included. Sample size was calculated to allow comparison between areas under receiver operating curves (ROC) between two different risk assessment tools. Sample size was calculated to be 1,363 assuming AUC1 = 0.65, AUC2 = 0.6, alpha = 0.05, power = 80%, correlation in positive group = 0.4, and correlation in negative group = 0.4. [ 21 ] HIV risk factors the following variables were considered in different HIV risk assessment tools to generate tool with better sensitivity, specificity, and positive predictive value (PPV+) being divorced, separated or widowed (DSW), having an HIV + spouse, having paid work within 12 months of the survey, slept away from home for at least a month within 12 months of the survey, had multiple sexual partners within 12 months of the survey, had ever sold sex, paid for sex within 12 months of the survey, had sexually transmitted infection (STI) within 12 months of the survey, diagnosed with cervical cancer, being a tuberculosis (TB) suspect within 12 months of the survey which meant having any of the following symptoms: cough, fever, night sweats or weight loss had TB disease, past or present, and being very sick for at least 3 months within 12 months of the survey, that is being too sick to work or do normal activities. HIV risk assessment tools examined: Four different hypothetical tools were considered in the validation: Tool 1: A tool that contained all conventional and any newly identified statistically significant risk factors that predicted HIV infection status in individuals never tested for HIV, Tool 2: A tool that contained only statistically significant risk factors that were identified by purposeful selection of variables using logistic regression model, Tool 3: A tool that contained conventional risk factors only, and Tool 4: A tool that contained conventional risk factors and a combination of newly identified risk factors. HIV testing was offered for everyone in the survey and performed for all consenting adults and adolescents > 14 years during the survey. Known HIV + status was further confirmed through the use of anti-retroviral markers within the blood. Those with anti-retroviral markers were excluded from the study. Data analysis Data was obtained from the public domain of PHIA website [ 22 ] and analyzed using Stata 14.0 statistical software. First, risk factors that had association with HIV infection among those who never tested for HIV were identified using Chi Square test. To develop scores for a risk assessment tool, appropriate screening items were selected and coded one when the risk factor was present and zero when it was not and the total score calculated for each individual as the sum of the numerical values of the screening items included within a tool. For instance, for the first screening tool where all risk factors were included, the minimum score was 0 while the potential maximum was 12. Chi Square test was also done to examine if having risk screening score of ≥ 1, ≥2, ≥ 3, or ≥ 4 was associated with HIV infection. Sampling weights were used to adjust statistical values taking into account complex sampling design used in PHIA surveys. [ 20 ] To determine the optimal cut-off for the screening tool that will enable identification of people at risk of HIV infection, a receiver operating characteristic curve (ROC) was plotted. The area under the ROC (Receiver Operating Characteristic) curve (AUC) and corresponding sensitivity, specificity, positive predictive (PPV) and negative predictive values (NPV) by using the screening tool at different level of scores were determined. ROC comparison statistics was used to statistically test equality between AUC of the different scores. For the score selected to be having the best combination of sensitivity, specificity, PPV, NPV and AUC, similar analysis was conducted to see if age, gender, and residence affected AUC. This was done by doing stratified analysis of AUC using the stated variables. Finally, to compare and select between the different risk assessment tools, test of quality of AUC was done. Number needed to test to identify one HIV infected person (NNT+) was also calculated to see if risk assessment tools reduced this number compared to universal testing. To select appropriate variables for the second tool, purposeful selection of variables was done using during regression model building. Those with p- value < 0.20 during bi-variable analysis were included in the final model and examined. Level of significance was set at 0.05. Ethics statement : all surveys had written informed consent, both for interview and blood collection for all participating adults. Parents consented for their children. All databases don’t have individual identifiers like names or addresses that can be used to identify people. Results Of 68,564 adults and adolescents > 14 years included in the Tanzania and Zambia PHIA studies, 55,340 had a matching interview and biomarker (including HIV testing) datasets. Of these, 39,103 (70.7%), had previously been tested for HIV and 181 (0.3%) had missing previous HIV testing data, and hence excluded from the analysis. Of the remaining 16,056, 15160 (94.4%) were tested for HIV. Excluding those who didn’t have sampling weights, the final study sample was 14,820. (Fig. 1 ) Compared to individuals who were eligible for inclusion in this study but had missing HIV testing result, those who were tested for HIV during the survey were likely to be older, male, from urban area. They were also less likely to have multiple sexual partners or sexually transmitted infection in the past 12 months. (Supporting Information Table 1) Of 14,820 study participants, 57.8% were men, and had a median age of 30 (IQR: 21–24). HIV prevalence was 2.3% (95% confidence interval (CI): 2.0-2.6). HIV prevalence was higher for the age category 25–49, among women, and in urban settings. All HIV risk factors, except for those with Presumptive TB, TB disease, or chronic illness, were found to be statistically significant predictors of HIV infection in individuals who were never tested for HIV. (Table 1 ) Figure 2 summarizes HIV prevalence by risk factor. The highest was recorded for people who sold sex (13.5%), followed by spouses of HIV infected adults (11%) and those who were divorced, separated, or widowed (6.1%). The presence of other risk factors had HIV testing yield ranging from 3.1%-5.5%. TB in the past 10 years had a testing yield of 33.3% for Zambia compared to 3.7% for those without TB in the past 10 years, p value 14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016–2017) Variable Response Total, n HIV+, n (%) P value Age 15–24 8,114 49 (0.6%) < 0.001 25–49 3,443 196 (5.7%) 50+ 3,263 95 (2.9%) Gender Male 8,567 162 (1.9%) 0.002 Female 6,253 176 (2.8%) Residence Urban 4,707 130 (2.8%) 0.026 Rural 10,113 207 (2.1%) Education No education 2,647 82 (3.1%) 0.001 Primary 8,182 197 (2.4%) Secondary 3,859 55 (1.4%) Tertiary 132 4 (3.1%) Wealth Quintile Lowest 3,398 73 (2.1%) 0.038 Secondary 3,412 70 (2.0%) Middle 3,081 73 (2.4%) Fourth 2,432 79 (3.2%) Highest 2,496 43 (1.7%) Marital status Single, Married 13,045 230 (1.8%) < 0.001 DSW $ 1,775 108 (6.1%) Spouse is Known to have HIV No 14,777 333 (2.3%) 0.001 Yes 43 5 (11.0%) Having Paid Work* No 9,692 172 (1.8%) 1 month* No 12,939 273 (2.1%) 0.015 Yes 1,881 65 (3.5%) Multiple Sexual Partners* No 12,970 282 (2.2%) 0.042 Yes 1,850 58 (3.1%) Ever Sold Sex No 14,786 333 (2.3%) < 0.001 Yes 34 5 (13.5%) Paid for Sex* No 14,100 304 (2.2%) < 0.001 Yes 720 36 (4.9%) Sexually transmitted infection* No 13,385 274 (2.0%) < 0.001 Yes 1,435 65 (4.5%) Has Cervical Cancer No 14,755 335 (2.3%) 0.025 Yes 65 4 (5.5%) Presumptive TB # * No 14,460 326 (2.3%) 0.215 Yes 360 12 (3.3%) TB disease, current or past No 14,639 330 (2.3%) 0.055 Yes 181 8 (4.4%) Sick for the past 3 months* No 14,242 317 (2.2%) 0.078 Yes 578 22 (3.8%) Total 14,820 338 (2.3%) $ Divorced, Separated, or Widowed; * within the last 12 months of the survey, # Cough, fever, night sweats, or weight loss Looking at the performance of Tool 1 at different risk score levels, those individuals having one or more risk factors were found to have an HIV prevalence of 3.2% which increased with increasing cut-off: 4.4%, 5.6%, 7.9% HIV prevalence for two, three, and four cut-off scores respectively. (Table 2 ) Fig. 3 indicates the ROC curve comparing the different cut-off points for Tool 1. Area under the curve (AUC) can be seen to reduce as the risk assessment cut-off increases. A score of ≥ 1 was found to have the highest sensitivity at 82.3% (95% CI: 78.6%-85.9%) with the next score of ≥ 2 having nearly half the sensitivity at 46.8% (42.0%-51.6%). The specificity was higher for a higher cut-off. Positive predictive value was higher for a higher cut-off point while negative predictive value was comparable between all cut-0ff scores. Table 2 Association of HIV Risk Scores with HIV Infection using a tool that contains all HIV Risk Factors for Adults and Adolescents > 14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016–2017) Risk Score Response Total, n HIV+, n (%) P value Score ≥ 1 No 6,122 59 (1.0%) < 0.001 Yes 8,698 279 (3.2%) Score ≥ 2 No 11,238 179 (1.6%) < 0.001 Yes 3,582 159 (4.4%) Score ≥ 3 No 13,556 267 (2.0%) < 0.001 Yes 1,264 71 (5.6%) Score ≥ 4 No 14,445 308 (2.1%) < 0.001 Yes 375 30 (7.9%) Compared to a cut-off score of ≥ 1, AUC was comparable with a cut-off score of ≥ 2 while it was lower for those with higher cut-off scores (p value < 0.001). (Table 3 ) The AUC was comparable by age, gender, and residence. (Table 4 ) Table 3 Sensitivity, Specificity, Positive Predictive Value (PPV+), and Negative Predictive Value (NPV-) for HIV Risk Screening Tool Containing Various Combinations of All Risk Factors Screening Tool Score* Sensitivity Specificity PPV NPV AUC** P value*** Score ≥ 1 82.3% (78.6%-85.9%) 41.9% (41.1%-42.7%) 3.2% (2.8%-3.6%) 99.0% (98.8%-99.3%) 0.6116 Score ≥ 2 46.8% (42.0%-51.6%) 76.4% (75.7%-77.1%) 4.4% (3.7%-5.1%) 98.4% (98.2%-98.6%) 0.6159 0.7137 Score ≥ 3 21.0% (17.1%-24.9%) 91.8% (91.3%-92.2%) 5.6% (4.3%-7.0%) 98.0% (97.8%-98.3%) 0.5599 < 0.001 Score ≥ 4 8.9% (6.2%-11.6%) 97.6% (97.4%-97.9%) 8.0% (5.1%-10.9%) 97.9% (97.6%-98.1%) 0.5332 < 0.001 * Score ≥ 1 means an individual who has one or more risk factors for HIV; ** AUC = Area under the Curve of a Receiver Operating Curve; *** P value compares AUC for a given score with the reference Score ≥ 1 Table 4 Comparison of Receiver Operating Characteristics Curve for HIV Risk Screening Tool (Score ≥ 1) by Age, Gender, and Residence for Adults and Adolescents > 14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016–2017) Variable Response Observation Area Under ROC Curve P value Age 15–24 7,868 0.5756 0.6033 25–49 3,507 0.5705 50+ 3,445 0.5478 Gender Male 7,945 0.6119 0.6448 Female 6,875 0.6212 Residence Urban 4,461 0.6070 0.7531 Rural 10,359 0.6137 Figure 4 summarizes relationship between eligibility, sensitivity, and PPV or HIV testing yield. Eligibility for HIV test decreased with increasing of risk score cut-offs: 56% would be eligible with a cut-off score of ≥ 1 while it was 2% for a cut-off score of ≥ 4. HIV testing positivity (PPV) and sensitivity or eligibility was negatively correlated with both going down with increasing cut-off score while PPV increased. In the tool that contained only statistically significant risk factors (Tool 2), being DSW (odds ratio (OR): 3.9 (95% CI: 2.9–5.2); p-value < 0.001), being spouse of a known HIV + person (OR: 6.1 (95% CI:2.0-19.1); p-value = 0.003), having history of selling sex for money (OR: 7.7 (95% CI:3.6–16.3); p-value < 0.001), having sexually transmitted infections in the past 12 months (OR: 2.1 (95% CI:1.4-3); p-value < 0.001), and working in the past 12 months (OR: 2.1 (95% CI:1.4-3); p-value < 0.001) were included in the final model. Tool 4 that contained customized risk factors, the combination of risk factors having paid work in the past year and sleeping away from home for more than a month in the past 12 months were combined as predictors in addition to conventional risk factors. Having a paid work and sleeping away from home were statistically significant predictors of undiagnosed HIV infection (OR: 1.8 (95% CI: 1.1-3.0)). Looking at the different risk assessment tools, all were statistically significant predictors of HIV infection with p-value < 0.001. For all tools, if none of the risk factors were present, HIV prevalence was low at 1.0-1.3%. (Table 5 ) Sensitivity was better for Tool 1 but the corresponding specificity was the lowest. AUC was better for all other tools as compared to this tool and the difference was much higher for Tools 3 and 4 (p-value < 0.001). (Table 6 ) PPV or HIV testing yield was highest for Tools 3 and 4 at 4.2% and 4.0%, respectively, if at least one risk factor was present. Tool 3 has the lowest proportion of people eligible for testing at 34%the highest being for tool 1 at 59%. (Table 5 ) Number needed to test (NNT+) was 24 for Tool 3 while it was 43 if universal testing was used. Table 5 Association of Risk Scores with HIV Infection using a tool that contains all HIV Risk Factors for Adults and Adolescents > 14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016–2017) Risk Assessment Tool Response Total, n HIV+, n (%) P value Tool 1: All Risk Factors: Score ≥ 1 No 6,122 59 (1.0%) < 0.001 Yes 8,698 279 (3.2%) Tool 2: Statistically Significant Risk Factors in final model: Score ≥ 1 No 7,850 90 (1.2%) < 0.001 Yes 6,970 247 (3.6%) Tool 3: Conventional Risk Factors: Score ≥ 1 No 9,717 123 (1.3%) < 0.001 Yes 5,103 215 (4.2%) Tool 4: Customized Tool: Score ≥ 1 No 9,294 117 (1.3%) < 0.001 Yes 5,526 221 (4.0%) Table 6 Sensitivity, Specificity, Positive Predictive Value (PPV+), and Negative Predictive Value (NPV-) for each potential HIV Risk Screening Tool for Adults and Adolescents > 14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016–2017) Risk Factor Selection Strategy Sensitivity Specificity PPV NPV AUC** P value*** Tool 1: All risk factors 82.3% (78.6%-85.9%) 41.9% (41.1%-42.7%) 3.2% (2.8%-3.6%) 99.0% (98.8%-99.3%) 0.6116 Tool 2: Statistically significant only # 73.4% (69.1%-77.6%) 53.6% (52.8%-54.4%) 3.6% (3.1%-4.0%) 98.9% (98.6%-99.1%) 0.6267 0.035 Tool 3: Conventional risk factors 63.5% (58.9%-68.1%) 66.2% (65.5%-67.0%) 4.2% (3.6%-4.8%) 98.7% (98.5%-98.9%) 0.6469 < 0.001 Tool 4: Customized tool* 65.5% (61.0%-70.1%) 63.4% (62.6%-64.2%) 4.0% (3.5%-4.5%) 98.7% (98.5%-99.0%) 0.6412 < 0.001 # Only those included in the final model were considered; *Customized tool = Conventional risk factors + Working for a payment in the past 12 months and Sleeping away from home for at least 1 month in the past 12 months of the survey. **AUC = Area under the Curve of a Receiver Operating Curve; *** P value compares AUC for a given risk assessment tool with the reference tool that contains all risk factors. Discussion We set out to validate HIV risk assessment tool used for adults. In that process we tried various combinations of risk factors in different tools for best possible outcome. The final tool we recommend for use contains conventional risk factors. That screening tool showed a moderate sensitivity and specificity for identifying infected adults at household level. Using this screening tool, the number needed to test to diagnose one HIV infected adult was 24 down from 43 if universal testing was used. Looking at individual risk factors, the prevalence of HIV in those who never tested for HIV remained to be high compared to those without risk factors except for TB related risk factors and chronic illness. Two risk factors that stood out with having testing yield of > 10% were selling sex for money and having an HIV + spouse. This is comparable to reported prevalence of 12–20% among FSWs in the study countries. [ 23 ] ICT for spouses records even higher testing yield at 32% in program settings. [ 24 ] Marital status is an important risk factor. Being divorced widowed or separated was found to be the third highest risk factor with a yield of 6.1%. DSWs are easily identifiable at community level and can be used to identify at risk people at community or facility level. It is already a risk factor in many countries. [ 25 , 26 ] Cervical cancer is an important risk factor since Human Papiloma Virus, which is a sexually transmitted viral infection, is the causative agent. [ 27 ] Co-infection with HIV was 5.5% in this study. Having multiple sexual partners was found to have a relatively lower prevalence at 3.1%. This may be due to the higher condom use during casual sex with a non-regular partner. [ 28 , 29 ] Lifetime TB disease was not statistically significant at the 0.05 cut-off. This should not be misinterpreted as TB not being a risk factor. Data on year of TB diagnosis was present only for Zambia and when we did analysis comparing TB diagnosed in the past 10 years to those who never had TB, or who had TB before 10 years, TB prevalence was much higher at 33.3% prevalence. That should be used in practice instead of lifetime TB disease. Presumptive TB was not predictor of HIV infection. That is probably because it was defined broadly especially for cough. A definition of cough > 2 weeks may make improve the positivity. In studies where the later definition was used, the positivity was found to be higher. [ 30 , 31 ] Adults having multiple risk factors were found to have high testing yield and were a small fraction of the total assessed. This should be further explored further to identify additional risk factors. A very good example in current use by different case finding and prevention programs is being long distance truck driver, who are likely to sleep away from home, and have multiple sexual partners including sex workers. [ 32 , 33 ] This study also provides some form of reference for the percentage of people who are potentially eligible for HIV testing fulfilling at least one of the conventional risk factors among those who never tested for HIV. In this study, 34.4% adults who never tested for HIV would be eligible for testing. That is around 9.1% of the initial number of adults interviewed. This provides a reference value with which to compare community HIV case finding interventions when such risk factors are used. However, it would not be advisable to test this much adults as it wouldn’t be cost effective. A more targeted approach focusing on sex workers and their clients, partners of known HIV + index cases, DSWs, and TB cases would be important starting points. [ 2 ] Eliciting some of the risk factors especially those related to sexual history may need some experience especially when implementing the risk assessment tool at community level. The use of health extension workers or community health workers who formally do health interventions may help. At facility level where these risk factors are often used maintaining quality of counselling needs to be ensured through ongoing training and on job coaching. Some of the risk factors are treated in speciality clinics like TB in TB clinic, or STI and cervical cancer in gynaecology clinics for women. This will make it easier to implement universal testing for these groups by providing integrated testing services. The large number of study participants was one of the strengths of the study. Missing data was minimal and was not related to the risk factors being studied. The performance of the final tool was found to be independent of age, gender, and residence making the use of the tool applicable in different scenarios. Some of the risk factors were captured a little different from what is used in actual settings. All parameters of screening tool are likely to improve if the presence of the following risk factors was determined for the past 10 years just like what we did with TB instead of just the past 12 months: multiple sexual partners, STI, and paid for sex. Conclusion Use of a screening tool containing conventional risk factors improved HIV testing yield compared to doing universal testing. The use of multiple risk factors to improve HIV testing yield should be explored further. List Of Abbreviations AUC: Area under the Curve; CI: Confidence Interval; DSW: Divorced, Separated, Widowed; HIV: Human Immunodeficiency Virus; ICT: Index case testing; IQR: Interquartile range; NNT: Number needed to test; NPV: Negative Predictive Value; OR: Odds ratio; PEPFAR: Presidents Emergency Plan for AIDS Relief; PHIA: Population-based HIV Impact Assessment; PPV: Positive Predictive Value; ROC: Receiver Operating Characteristics; STI: Sexually transmitted Disease; TB: Tuberculosis; WHO: World Health Organization; Declarations Ethics approval and consent to participate Both PHIA surveys had written informed consent, both for interview and blood collection for all participating adults. Parents consented for adolescents. All datasets don’t have individual identifiers like names or addresses that can be used to identify people. In addition, the study got a non-human subject determination from the Office of International Research Ethics of FHI360. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article as Additional File 1 in Excel format. Competing interests The authors declare that they have no competing interests. Funding The authors received no funding to conduct this study. Authors' contributions KDY originated the research idea, collected and analyzed the data; KDY & JM contributed to data analysis and writing the manuscript; All authors read and approved the final manuscript. Acknowledgements Not applicable. Author details 1 FHI360, Addis Ababa, Ethiopia; 2 FHI360, Washington DC, USA References World Health Organisation: WHO Guidelines on HIV Testing . 2015. World Health Organisation: Consolidated Guidelines on HIV Prevention, Diagnosis, Treatment And Care For Key Populations . 2016. World Health Organisation: Consolidated HIV Prevention, Care and Treatment Guideline . 2016. 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Wu ES, Urban RR, Krantz EM, Mugisha NM, Nakisige C, Schwartz SM, Gray HJ, Casper C: The association between HIV infection and cervical cancer presentation and survival in Uganda . Gynecologic oncology reports 2020, 31 :100516. Dalal S, Johnson C, Fonner V, Kennedy CE, Siegfried N, Figueroa C, Baggaley R: Reaching people with undiagnosed HIV infection through assisted partner notification . AIDS (London, England) 2017, 31 (17):2436. Smith DK, Pan Y, Rose CE, Pals SL, Mehta SH, Kirk GD, Herbst JH: A brief screening tool to assess the risk of contracting HIV infection among active injection drug users . Journal of addiction medicine 2015, 9 (3):226. Haukoos JS, Hopkins E, Bucossi MM, Lyons MS, Rothman RE, White DA, Al-Tayyib AA, Bradley-Springer L, Campbell JD, Sabel AL: Validation of a quantitative HIV risk prediction tool using a national HIV testing cohort . Journal of acquired immune deficiency syndromes (1999) 2015, 68 (5):599. Ministry of Health Z: Zambia Population-based HIV Impact Assessment (ZAMPHIA) 2016: Final Report. Lusaka . 2019. (ZAC). TCfATZAC: Tanzania HIV Impact Survey (THIS) 2016-2017: Final Report. Dar es Salaam, Tanzania. 2018. Population-based HIV Impact Assessment (PHIA) Data Use Manual. New York NJ. MedCalc Statistical Software version 19.4.1 (MedCalc Software Ltd -O, -Belgium; https:// www.medcalc.org; 2020) . The PHIA Project: Population-Based HIV Impact Assessment Datasets (accessed from: https://phia-data.icap.columbia.edu/files) . 2020. Chanda MM, Ortblad KF, Mwale M, Chongo S, Kanchele C, Kamungoma N, Fullem A, Dunn C, Barresi LG, Harling G: HIV self-testing among female sex workers in Zambia: a cluster randomized controlled trial . PLoS medicine 2017, 14 (11):e1002442. Mwango L, Mujansi M, Chipukuma J, Phiri B, Sakala H, Nyirongo N, Sivile S, Sinjani M, Lavoie M-C, Claassen C: Reaching the unreachable: early results from index testing in Zambia in the CIRKUITS project . In: JOURNAL OF THE INTERNATIONAL AIDS SOCIETY: 2019 : JOHN WILEY & SONS LTD THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W …; 2019: 13-14. Ministry of Health: National Comprehensive HIV Prevention, Care and Treatment Training for Health care Providers . 2017. Ministry of Health: The Kenya HIV Testing Services Guidelines (from http://www.hivst.org/files1/kenyahtsguidelines20151-160119080906.pdf) . 2015. Mayo Foundation for Medical Education and Research: Cervical cancer: symptoms and cause (from: https:// www.mayoclinic.org/diseases-conditions/cervical-cancer/symptoms-causes/syc-20352501) . 2020. Zambia Statistics Agency - ZSA, Ministry of Health - MOH, University Teaching Hospital Virology Laboratory - UTH-VL, ICF: Zambia Demographic and Health Survey 2018 . In . Lusaka, Zambia: ZSA, MOH, UTH-VL and ICF; 2020. Ministry of Health CD, Gender, Elderly, Children - MoHCDGEC/Tanzania Mainland, Ministry of Health - MoH/Zanzibar, National Bureau of Statistics - NBS/Tanzania, Office of Chief Government Statistician - OCGS/Zanzibar, ICF: Tanzania Demographic and Health Survey and Malaria Indicator Survey 2015-2016 . In . Dar es Salaam, Tanzania: MoHCDGEC, MoH, NBS, OCGS, and ICF; 2016. Yotebieng M, Wenzi LK, Basaki E, Batumbula ML, Tabala M, Mungoyo E, Mangala R, Behets F: Provider-Initiated HIV testing and counseling among patients with presumptive tuberculosis in Democratic Republic of Congo . The Pan African Medical Journal 2016, 25 . Kyaw KWY, Kyaw NTT, Kyi MS, Aye S, Harries AD, Kumar AM, Oo NL, Satyanarayana S, Aung ST: HIV testing uptake and HIV positivity among presumptive tuberculosis patients in Mandalay, Myanmar, 2014-2017 . PloS one 2020, 15 (6):e0234429. Delany-Moretlwe S, Bello B, Kinross P, Oliff M, Chersich M, Kleinschmidt I, Rees H: HIV prevalence and risk in long-distance truck drivers in South Africa: a national cross-sectional survey . International journal of STD & AIDS 2014, 25 (6):428-438. Botão C, Horth RZ, Frank H, Cummings B, Inguane C, Sathane I, McFarland W, Raymond HF, Young PW: Prevalence of HIV and associated risk factors among long distance truck drivers in Inchope, Mozambique, 2012 . AIDS and Behavior 2016, 20 (4):811-820. Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.zip SupportingInformationTable.pdf Supporting Information Table 1. Comparison between Study Participants and non-Participants based on Socio-demographic Characteristics and HIV Risk Factors Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-209246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":10448094,"identity":"ddcf2d73-0106-4d51-a4fa-ae03110681d9","order_by":0,"name":"Kesetebirhan Delele Yirdaw","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACCRDxwUCinp+BgY14LYwzKmwSJBtI0cLMcyYtweAAsVok249fe8DbdjjP+EbyswcfKhjk+cUO4NcizZNTbiDZdrjY7EaaueGMMwyGM2cn4NciJ8GTJmHYdphx240EM2neNoYEg9vEaEkEatk8I/0bcVqkJdiPSRw4k5a4QSKHSFske3LYDRsqbIwlzrwpk5xxRoKwXySOH3/2+I+BhBx/e/o2iQ8VNvL80gS0MDDwmEFoAbBKCULKQYD9GYTmP0CM6lEwCkbBKBiJAAD2fUL+xiJ7bwAAAABJRU5ErkJggg==","orcid":"","institution":"Family Health International 360","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kesetebirhan","middleName":"Delele","lastName":"Yirdaw","suffix":""},{"id":10448095,"identity":"b409dc92-5248-4923-a6bf-96bc67c03d2b","order_by":1,"name":"Justin Mandala","email":"","orcid":"","institution":"Family Health International 360","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Justin","middleName":"","lastName":"Mandala","suffix":""}],"badges":[],"createdAt":"2021-02-05 06:44:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-209246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-209246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5681226,"identity":"6520b8c9-7267-423b-9c89-c19424555cf6","added_by":"auto","created_at":"2021-02-05 20:26:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70738,"visible":true,"origin":"","legend":"Selection of Study Sample","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/5431a586e0d851d7a70a64ed.jpg"},{"id":5681361,"identity":"85aba41d-917b-4d04-96e3-59657584cb62","added_by":"auto","created_at":"2021-02-05 20:29:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88719,"visible":true,"origin":"","legend":"HIV Testing Yield by Risk Factors among Adults and Adolescents \u003e14 years who were never tested for HIV before PHIA surveys conducted in Zambia (2016) and Tanzania (2016-2017)","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/3059b1f87c4b696420e34314.jpg"},{"id":5681228,"identity":"22a77169-4bb8-4df7-a143-d80cccd7cdfe","added_by":"auto","created_at":"2021-02-05 20:26:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50856,"visible":true,"origin":"","legend":"Receiver Operating Characteristics Curve by HIV Risk Scores for Adults and Adolescents \u003e14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016-2017). For each cut-off, sensitivity, specificity, and area under the curve are indicated.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/ece427e071f2f9ba3b679649.jpg"},{"id":5681230,"identity":"66796225-81ea-48ff-90d9-5492d92cbb23","added_by":"auto","created_at":"2021-02-05 20:26:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59488,"visible":true,"origin":"","legend":"Relationship between Eligibility, Sensitivity, and HIV Testing Yield for a Risk Assessment Tool that contains all Risk Factors for Adults and Adolescents \u003e14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016-2017)","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/ddc41d969e389cacf44b309b.jpg"},{"id":13655143,"identity":"48d9e36d-2d7f-47c1-bebe-6b486e5e1739","added_by":"auto","created_at":"2021-09-17 10:01:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/e7c34f70-9c25-4ac7-befe-472eb5580da9.pdf"},{"id":5681362,"identity":"ef25bf06-3bfc-41df-92e4-f073c3306c86","added_by":"auto","created_at":"2021-02-05 20:29:22","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3154327,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.zip","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/9d3916ea359296d30a217c35.zip"},{"id":5681229,"identity":"a554775b-973b-474d-a431-12d7b28ee43c","added_by":"auto","created_at":"2021-02-05 20:26:22","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":147249,"visible":true,"origin":"","legend":"Supporting Information Table 1. Comparison between Study Participants and non-Participants based on Socio-demographic Characteristics and HIV Risk Factors","description":"","filename":"SupportingInformationTable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-209246/v1/9a47ac87afa6a4bfeed16180.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of HIV Risk Screening Tool to Identify Infected Adults and Adolescents \u003e14 years at Community Level","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHIV testing is the gate way for case finding, care and treatment as well as prevention services for high risk individuals. [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] Over the years remarkable progress has been made to diagnose infected people and put them on treatment. To date, Eastern and Southern African countries have coverage of 87% (77%-95%) for the first 90 while the coverage is 68% (54%-87%) for Central and Western African countries. [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] This correlates with high uptake of HIV testing across these countries. Prior HIV testing among surveyed men and women 15\u0026ndash;49 years was 62% and 74% for Eastern, Southern and Central African countries, respectively from 2015\u0026ndash;2018. It was much lower for Western African countries at 31% for women and 16% for men. [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eMaintaining such high testing coverage or conducting door-to-door testing in high risk communities is not feasible because of the limitation of funding available for HIV programs considering flattening of global support for HIV programs especially that of PEPFAR over the past 10 years. [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] As a result of that, a strategic shift has been made to implement targeted HIV testing with the aim of getting high testing yield per dollar spent on HIV test kit in many country HIV programs including high burden countries with the aim of putting as many infected people on treatment and reducing new infection and mortality in the process. [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eA number of HIV risk factors have been identified and in use to effect targeted HIV testing of at risk people. The World Health Organization (WHO) recommends HIV testing for clients having sexually transmitted infection (STI), viral hepatitis, tuberculosis (TB); key populations including commercial sex workers, men having sex with men, and IV drug users; clients with symptoms or medical conditions that could indicate HIV infection, including presumed and confirmed TB cases. [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e] Other risk factors known to increase risk of HIV infection include having multiple sexual partners [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], being divorced, separated or widowed (DSW) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], history of being a client of a sex worker [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], having cervical Ca [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], being partners with known infected person [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eA number of HIV risk assessment tools were validated in different settings in an effort to determine best options to identify HIV infected adults. [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] These tools often don\u0026rsquo;t include risk factors recommended by the WHO and in use in high prevalence countries. Knowing the performance and limitation of a risk screening tool containing all common HIV risk factors is crucial to determine case finding strategies that better fit routine implementation setting and assess quality of testing services both in clinical and community settings. This study aims to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003edetermine the performance of a hypothetical HIV risk assessment tool that contains conventional HIV risk factors to identify undiagnosed HIV\u0026thinsp;+\u0026thinsp;adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edetermine the performance of a hypothetical HIV risk assessment tool that contains all potential HIV risk factors to identify undiagnosed HIV\u0026thinsp;+\u0026thinsp;adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edetermine the performance of a hypothetical HIV risk assessment tool that contains only statistically significant HIV risk factors to identify undiagnosed HIV positive adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edetermine which of the above three tools is better in terms of overall performance to identify undiagnosed HIV positive adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edetermine if the presence of multiple HIV risk factors in one person improves performance of risk assessment tool to identify undiagnosed HIV positive adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy setting and design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a cross sectional study based on secondary analysis of data from two community based household surveys that were conducted in Zambia (2016) and Tanzania (2016\u0026ndash;2017). These surveys were Population-Based HIV Impact Assessment (PHIA) studies conducted with PEPFAR support. [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe surveys were conducted during 2016\u0026ndash;2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eadolescents and adults\u0026thinsp;\u0026gt;\u0026thinsp;14 years who have matching interview and biomarker datasets (HIV testing result) and who had never tested for HIV prior the survey were included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewas calculated to allow comparison between areas under receiver operating curves (ROC) between two different risk assessment tools. Sample size was calculated to be 1,363 assuming AUC1\u0026thinsp;=\u0026thinsp;0.65, AUC2\u0026thinsp;=\u0026thinsp;0.6, alpha\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;80%, correlation in positive group\u0026thinsp;=\u0026thinsp;0.4, and correlation in negative group\u0026thinsp;=\u0026thinsp;0.4. [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIV risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe following variables were considered in different HIV risk assessment tools to generate tool with better sensitivity, specificity, and positive predictive value (PPV+)\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ebeing divorced, separated or widowed (DSW),\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehaving an HIV\u0026thinsp;+\u0026thinsp;spouse,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehaving paid work within 12 months of the survey,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eslept away from home for at least a month within 12 months of the survey,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehad multiple sexual partners within 12 months of the survey,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehad ever sold sex,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epaid for sex within 12 months of the survey,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehad sexually transmitted infection (STI) within 12 months of the survey,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ediagnosed with cervical cancer,\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebeing a tuberculosis (TB) suspect within 12 months of the survey which meant having any of the following symptoms: cough, fever, night sweats or weight loss\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ehad TB disease, past or present, and\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebeing very sick for at least 3 months within 12 months of the survey, that is being too sick to work or do normal activities.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eHIV risk assessment tools examined:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour different hypothetical tools were considered in the validation:\u003c/p\u003e\n\u003cp\u003eTool 1: A tool that contained all conventional and any newly identified statistically significant risk factors that predicted HIV infection status in individuals never tested for HIV,\u003c/p\u003e\n\u003cp\u003eTool 2: A tool that contained only statistically significant risk factors that were identified by purposeful selection of variables using logistic regression model,\u003c/p\u003e\n\u003cp\u003eTool 3: A tool that contained conventional risk factors only, and\u003c/p\u003e\n\u003cp\u003eTool 4: A tool that contained conventional risk factors and a combination of newly identified risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIV testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewas offered for everyone in the survey and performed for all consenting adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years during the survey. Known HIV\u0026thinsp;+\u0026thinsp;status was further confirmed through the use of anti-retroviral markers within the blood. Those with anti-retroviral markers were excluded from the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was obtained from the public domain of PHIA website [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] and analyzed using Stata 14.0 statistical software. First, risk factors that had association with HIV infection among those who never tested for HIV were identified using Chi Square test. To develop scores for a risk assessment tool, appropriate screening items were selected and coded one when the risk factor was present and zero when it was not and the total score calculated for each individual as the sum of the numerical values of the screening items included within a tool. For instance, for the first screening tool where all risk factors were included, the minimum score was 0 while the potential maximum was 12. Chi Square test was also done to examine if having risk screening score of \u0026ge;\u0026thinsp;1, \u0026ge;2, \u0026ge;\u0026thinsp;3, or \u0026ge;\u0026thinsp;4 was associated with HIV infection. Sampling weights were used to adjust statistical values taking into account complex sampling design used in PHIA surveys. [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eTo determine the optimal cut-off for the screening tool that will enable identification of people at risk of HIV infection, a receiver operating characteristic curve (ROC) was plotted. The area under the ROC (Receiver Operating Characteristic) curve (AUC) and corresponding sensitivity, specificity, positive predictive (PPV) and negative predictive values (NPV) by using the screening tool at different level of scores were determined. ROC comparison statistics was used to statistically test equality between AUC of the different scores. For the score selected to be having the best combination of sensitivity, specificity, PPV, NPV and AUC, similar analysis was conducted to see if age, gender, and residence affected AUC. This was done by doing stratified analysis of AUC using the stated variables.\u003c/p\u003e\n\u003cp\u003eFinally, to compare and select between the different risk assessment tools, test of quality of AUC was done. Number needed to test to identify one HIV infected person (NNT+) was also calculated to see if risk assessment tools reduced this number compared to universal testing. To select appropriate variables for the second tool, purposeful selection of variables was done using during regression model building. Those with p- value\u0026thinsp;\u0026lt;\u0026thinsp;0.20 during bi-variable analysis were included in the final model and examined. Level of significance was set at 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eall surveys had written informed consent, both for interview and blood collection for all participating adults. Parents consented for their children. All databases don\u0026rsquo;t have individual identifiers like names or addresses that can be used to identify people.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf 68,564 adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years included in the Tanzania and Zambia PHIA studies, 55,340 had a matching interview and biomarker (including HIV testing) datasets. Of these, 39,103 (70.7%), had previously been tested for HIV and 181 (0.3%) had missing previous HIV testing data, and hence excluded from the analysis. Of the remaining 16,056, 15160 (94.4%) were tested for HIV. Excluding those who didn\u0026rsquo;t have sampling weights, the final study sample was 14,820. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eCompared to individuals who were eligible for inclusion in this study but had missing HIV testing result, those who were tested for HIV during the survey were likely to be older, male, from urban area. They were also less likely to have multiple sexual partners or sexually transmitted infection in the past 12 months. (Supporting Information Table\u0026nbsp;1)\u003c/p\u003e\n\u003cp\u003eOf 14,820 study participants, 57.8% were men, and had a median age of 30 (IQR: 21\u0026ndash;24). HIV prevalence was 2.3% (95% confidence interval (CI): 2.0-2.6). HIV prevalence was higher for the age category 25\u0026ndash;49, among women, and in urban settings. All HIV risk factors, except for those with Presumptive TB, TB disease, or chronic illness, were found to be statistically significant predictors of HIV infection in individuals who were never tested for HIV. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes HIV prevalence by risk factor. The highest was recorded for people who sold sex (13.5%), followed by spouses of HIV infected adults (11%) and those who were divorced, separated, or widowed (6.1%). The presence of other risk factors had HIV testing yield ranging from 3.1%-5.5%. TB in the past 10 years had a testing yield of 33.3% for Zambia compared to 3.7% for those without TB in the past 10 years, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (data not shown).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDeterminants of HIV Infection among adults and adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016\u0026ndash;2017)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal, n\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHIV+, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8,114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e196 (5.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,263\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95 (2.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8,567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e162 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6,253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e176 (2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,707\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e130 (2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10,113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e207 (2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82 (3.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8,182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e197 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55 (1.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTertiary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4 (3.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWealth Quintile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLowest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,398\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73 (2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFourth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHighest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43 (1.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSingle, Married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13,045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e230 (1.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDSW\u003csup\u003e\u003cstrong\u003e$\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108 (6.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpouse is Known to have HIV\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e333 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5 (11.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHaving Paid Work*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,692\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e172 (1.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e166 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSlept Away from Home for \u0026gt;\u0026thinsp;1 month*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12,939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e273 (2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,881\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple Sexual Partners*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12,970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e282 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58 (3.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEver Sold Sex\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,786\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e333 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5 (13.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePaid for Sex*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e304 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSexually transmitted infection*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13,385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e274 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHas Cervical Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e335 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4 (5.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePresumptive TB\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e326 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.215\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTB disease, current or past\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e330 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8 (4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSick for the past 3 months*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e317 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22 (3.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,820\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e338 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cstrong\u003e$\u003c/strong\u003e\u003c/sup\u003eDivorced, Separated, or Widowed; \u003cstrong\u003e*\u003c/strong\u003ewithin the last 12 months of the survey,\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003eCough, fever, night sweats, or weight loss\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLooking at the performance of Tool 1 at different risk score levels, those individuals having one or more risk factors were found to have an HIV prevalence of 3.2% which increased with increasing cut-off: 4.4%, 5.6%, 7.9% HIV prevalence for two, three, and four cut-off scores respectively. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e indicates the ROC curve comparing the different cut-off points for Tool 1. Area under the curve (AUC) can be seen to reduce as the risk assessment cut-off increases. A score of \u0026ge;\u0026thinsp;1 was found to have the highest sensitivity at 82.3% (95% CI: 78.6%-85.9%) with the next score of \u0026ge;\u0026thinsp;2 having nearly half the sensitivity at 46.8% (42.0%-51.6%). The specificity was higher for a higher cut-off. Positive predictive value was higher for a higher cut-off point while negative predictive value was comparable between all cut-0ff scores.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssociation of HIV Risk Scores with HIV Infection using a tool that contains all HIV Risk Factors for Adults and Adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016\u0026ndash;2017)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRisk Score\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal, n\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHIV+, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6,122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8,698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e279 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11,238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e179 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,582\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e159 (4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13,556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e267 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1,264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e71 (5.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14,445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e308 (2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30 (7.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to a cut-off score of \u0026ge;\u0026thinsp;1, AUC was comparable with a cut-off score of \u0026ge;\u0026thinsp;2 while it was lower for those with higher cut-off scores (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) The AUC was comparable by age, gender, and residence. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSensitivity, Specificity, Positive Predictive Value (PPV+), and Negative Predictive Value (NPV-) for HIV Risk Screening Tool Containing Various Combinations of All Risk Factors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScreening Tool Score*\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePPV\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNPV\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC**\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value***\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e82.3% (78.6%-85.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e41.9% (41.1%-42.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e3.2% (2.8%-3.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e99.0% (98.8%-99.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e46.8% (42.0%-51.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e76.4% (75.7%-77.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e4.4% (3.7%-5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e98.4% (98.2%-98.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e21.0% (17.1%-24.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e91.8% (91.3%-92.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e5.6% (4.3%-7.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e98.0% (97.8%-98.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e8.9% (6.2%-11.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e97.6% (97.4%-97.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e8.0% (5.1%-10.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e97.9% (97.6%-98.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e* Score\u0026thinsp;\u0026ge;\u0026thinsp;1 means an individual who has one or more risk factors for HIV; ** AUC\u0026thinsp;=\u0026thinsp;Area under the Curve of a Receiver Operating Curve;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e*** P value compares AUC for a given score with the reference Score\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of Receiver Operating Characteristics Curve for HIV Risk Screening Tool (Score\u0026thinsp;\u0026ge;\u0026thinsp;1) by Age, Gender, and Residence for Adults and Adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016\u0026ndash;2017)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObservation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eArea Under ROC Curve\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6033\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3,445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6448\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6,875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4,461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7531\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10,359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes relationship between eligibility, sensitivity, and PPV or HIV testing yield. Eligibility for HIV test decreased with increasing of risk score cut-offs: 56% would be eligible with a cut-off score of \u0026ge;\u0026thinsp;1 while it was 2% for a cut-off score of \u0026ge;\u0026thinsp;4. HIV testing positivity (PPV) and sensitivity or eligibility was negatively correlated with both going down with increasing cut-off score while PPV increased.\u003c/p\u003e\n\u003cp\u003eIn the tool that contained only statistically significant risk factors (Tool 2), being DSW (odds ratio (OR): 3.9 (95% CI: 2.9\u0026ndash;5.2); p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), being spouse of a known HIV\u0026thinsp;+\u0026thinsp;person (OR: 6.1 (95% CI:2.0-19.1); p-value\u0026thinsp;=\u0026thinsp;0.003), having history of selling sex for money (OR: 7.7 (95% CI:3.6\u0026ndash;16.3); p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), having sexually transmitted infections in the past 12 months (OR: 2.1 (95% CI:1.4-3); p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and working in the past 12 months (OR: 2.1 (95% CI:1.4-3); p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were included in the final model. Tool 4 that contained customized risk factors, the combination of risk factors having paid work in the past year and sleeping away from home for more than a month in the past 12 months were combined as predictors in addition to conventional risk factors. Having a paid work and sleeping away from home were statistically significant predictors of undiagnosed HIV infection (OR: 1.8 (95% CI: 1.1-3.0)).\u003c/p\u003e\n\u003cp\u003eLooking at the different risk assessment tools, all were statistically significant predictors of HIV infection with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. For all tools, if none of the risk factors were present, HIV prevalence was low at 1.0-1.3%. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) Sensitivity was better for Tool 1 but the corresponding specificity was the lowest. AUC was better for all other tools as compared to this tool and the difference was much higher for Tools 3 and 4 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) PPV or HIV testing yield was highest for Tools 3 and 4 at 4.2% and 4.0%, respectively, if at least one risk factor was present. Tool 3 has the lowest proportion of people eligible for testing at 34%the highest being for tool 1 at 59%. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) Number needed to test (NNT+) was 24 for Tool 3 while it was 43 if universal testing was used.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssociation of Risk Scores with HIV Infection using a tool that contains all HIV Risk Factors for Adults and Adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016\u0026ndash;2017)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRisk Assessment Tool\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal, n\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHIV+, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 1: All Risk Factors: Score\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6,122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8,698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e279 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 2: Statistically Significant Risk Factors in final model: Score\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90 (1.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6,970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e247 (3.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 3: Conventional Risk Factors: Score\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,717\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e123 (1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e215 (4.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 4: Customized Tool: Score\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e117 (1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5,526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e221 (4.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSensitivity, Specificity, Positive Predictive Value (PPV+), and Negative Predictive Value (NPV-) for each potential HIV Risk Screening Tool for Adults and Adolescents\u0026thinsp;\u0026gt;\u0026thinsp;14 years who were never tested for HIV before PHIA Surveys conducted in Zambia (2016) and Tanzania (2016\u0026ndash;2017)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRisk Factor Selection Strategy\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePPV\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNPV\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC**\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value***\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 1: All risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e82.3% (78.6%-85.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e41.9% (41.1%-42.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e3.2% (2.8%-3.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e99.0% (98.8%-99.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 2: Statistically significant only\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e73.4% (69.1%-77.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e53.6% (52.8%-54.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e3.6% (3.1%-4.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e98.9% (98.6%-99.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 3: Conventional risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e63.5% (58.9%-68.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e66.2% (65.5%-67.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e4.2% (3.6%-4.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e98.7% (98.5%-98.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTool 4: Customized tool*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e65.5% (61.0%-70.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e63.4% (62.6%-64.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e4.0% (3.5%-4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cp\u003e98.7% (98.5%-99.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003e#\u003c/sup\u003eOnly those included in the final model were considered; *Customized tool\u0026thinsp;=\u0026thinsp;Conventional risk factors\u0026thinsp;+\u0026thinsp;Working for a payment in the past 12 months and Sleeping away from home for at least 1 month in the past 12 months of the survey. **AUC\u0026thinsp;=\u0026thinsp;Area under the Curve of a Receiver Operating Curve;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e*** P value compares AUC for a given risk assessment tool with the reference tool that contains all risk factors.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe set out to validate HIV risk assessment tool used for adults. In that process we tried various combinations of risk factors in different tools for best possible outcome. The final tool we recommend for use contains conventional risk factors. That screening tool showed a moderate sensitivity and specificity for identifying infected adults at household level. Using this screening tool, the number needed to test to diagnose one HIV infected adult was 24 down from 43 if universal testing was used.\u003c/p\u003e\n\u003cp\u003eLooking at individual risk factors, the prevalence of HIV in those who never tested for HIV remained to be high compared to those without risk factors except for TB related risk factors and chronic illness. Two risk factors that stood out with having testing yield of \u0026gt;\u0026thinsp;10% were selling sex for money and having an HIV\u0026thinsp;+\u0026thinsp;spouse. This is comparable to reported prevalence of 12\u0026ndash;20% among FSWs in the study countries. [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] ICT for spouses records even higher testing yield at 32% in program settings. [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] Marital status is an important risk factor. Being divorced widowed or separated was found to be the third highest risk factor with a yield of 6.1%. DSWs are easily identifiable at community level and can be used to identify at risk people at community or facility level. It is already a risk factor in many countries. [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] Cervical cancer is an important risk factor since Human Papiloma Virus, which is a sexually transmitted viral infection, is the causative agent. [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] Co-infection with HIV was 5.5% in this study. Having multiple sexual partners was found to have a relatively lower prevalence at 3.1%. This may be due to the higher condom use during casual sex with a non-regular partner. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eLifetime TB disease was not statistically significant at the 0.05 cut-off. This should not be misinterpreted as TB not being a risk factor. Data on year of TB diagnosis was present only for Zambia and when we did analysis comparing TB diagnosed in the past 10 years to those who never had TB, or who had TB before 10 years, TB prevalence was much higher at 33.3% prevalence. That should be used in practice instead of lifetime TB disease.\u003c/p\u003e\n\u003cp\u003ePresumptive TB was not predictor of HIV infection. That is probably because it was defined broadly especially for cough. A definition of cough\u0026thinsp;\u0026gt;\u0026thinsp;2 weeks may make improve the positivity. In studies where the later definition was used, the positivity was found to be higher. [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eAdults having multiple risk factors were found to have high testing yield and were a small fraction of the total assessed. This should be further explored further to identify additional risk factors. A very good example in current use by different case finding and prevention programs is being long distance truck driver, who are likely to sleep away from home, and have multiple sexual partners including sex workers. [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eThis study also provides some form of reference for the percentage of people who are potentially eligible for HIV testing fulfilling at least one of the conventional risk factors among those who never tested for HIV. In this study, 34.4% adults who never tested for HIV would be eligible for testing. That is around 9.1% of the initial number of adults interviewed. This provides a reference value with which to compare community HIV case finding interventions when such risk factors are used. However, it would not be advisable to test this much adults as it wouldn\u0026rsquo;t be cost effective. A more targeted approach focusing on sex workers and their clients, partners of known HIV\u0026thinsp;+\u0026thinsp;index cases, DSWs, and TB cases would be important starting points. [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eEliciting some of the risk factors especially those related to sexual history may need some experience especially when implementing the risk assessment tool at community level. The use of health extension workers or community health workers who formally do health interventions may help. At facility level where these risk factors are often used maintaining quality of counselling needs to be ensured through ongoing training and on job coaching. Some of the risk factors are treated in speciality clinics like TB in TB clinic, or STI and cervical cancer in gynaecology clinics for women. This will make it easier to implement universal testing for these groups by providing integrated testing services.\u003c/p\u003e\n\u003cp\u003eThe large number of study participants was one of the strengths of the study. Missing data was minimal and was not related to the risk factors being studied. The performance of the final tool was found to be independent of age, gender, and residence making the use of the tool applicable in different scenarios. Some of the risk factors were captured a little different from what is used in actual settings. All parameters of screening tool are likely to improve if the presence of the following risk factors was determined for the past 10 years just like what we did with TB instead of just the past 12 months: multiple sexual partners, STI, and paid for sex.\u003c/p\u003e"},{"header":"Conclusion","content":" \u003cp\u003eUse of a screening tool containing conventional risk factors improved HIV testing yield compared to doing universal testing. The use of multiple risk factors to improve HIV testing yield should be explored further.\u003c/p\u003e "},{"header":"List Of Abbreviations","content":"\u003cp\u003eAUC: Area under the Curve; CI: Confidence Interval; DSW: Divorced, Separated, Widowed; HIV: Human Immunodeficiency Virus; ICT: Index case testing; IQR: Interquartile range; NNT: Number needed to test; NPV: Negative Predictive Value; OR: Odds ratio; PEPFAR: Presidents Emergency Plan for AIDS Relief; PHIA: Population-based HIV Impact Assessment; PPV: Positive Predictive Value; ROC: Receiver Operating Characteristics; STI: Sexually transmitted Disease; TB: Tuberculosis; WHO: World Health Organization;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth PHIA surveys had written informed consent, both for interview and blood collection for all participating adults. Parents consented for adolescents. All datasets don\u0026rsquo;t have individual identifiers like names or addresses that can be used to identify people. In addition, the study got a non-human subject determination from the Office of International Research Ethics of FHI360.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article as Additional File 1 in Excel format.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding to conduct this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKDY originated the research idea, collected and analyzed the data; KDY \u0026amp; JM contributed to data analysis and writing the manuscript; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor details\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eFHI360, Addis Ababa, Ethiopia; \u003csup\u003e2 \u003c/sup\u003eFHI360, Washington DC, USA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organisation: \u003cstrong\u003eWHO Guidelines on HIV Testing\u003c/strong\u003e. 2015.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation: \u003cstrong\u003eConsolidated Guidelines on HIV Prevention, Diagnosis, Treatment And Care For Key Populations\u003c/strong\u003e. 2016.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation: \u003cstrong\u003eConsolidated HIV Prevention, Care and Treatment Guideline\u003c/strong\u003e. 2016.\u003c/li\u003e\n\u003cli\u003eUNAIDS: \u003cstrong\u003eAIDS Info: Progress towards 90-90-90 targets (https://aidsinfo.unaids.org/)\u003c/strong\u003e. 2020.\u003c/li\u003e\n\u003cli\u003eICF: \u003cstrong\u003eThe DHS Program STATcompiler. \u003c/strong\u003e\u003ca href=\"http://www.statcompiler.com\"\u003e\u003cstrong\u003ehttp://www.statcompiler.com\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e. 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In: \u003cem\u003eJOURNAL OF THE INTERNATIONAL AIDS SOCIETY: 2019\u003c/em\u003e: JOHN WILEY \u0026amp; SONS LTD THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W\u0026nbsp;\u0026hellip;; 2019: 13-14.\u003c/li\u003e\n\u003cli\u003eMinistry of Health: \u003cstrong\u003eNational Comprehensive HIV Prevention, Care and Treatment Training for Health care Providers\u003c/strong\u003e. 2017.\u003c/li\u003e\n\u003cli\u003eMinistry of Health: \u003cstrong\u003eThe Kenya HIV Testing Services Guidelines (from \u003c/strong\u003e\u003ca href=\"http://www.hivst.org/files1/kenyahtsguidelines20151-160119080906.pdf)\"\u003e\u003cstrong\u003ehttp://www.hivst.org/files1/kenyahtsguidelines20151-160119080906.pdf)\u003c/strong\u003e\u003c/a\u003e. 2015.\u003c/li\u003e\n\u003cli\u003eMayo Foundation for Medical Education and Research: \u003cstrong\u003eCervical cancer: symptoms and cause (from: https://\u003c/strong\u003e\u003ca href=\"http://www.mayoclinic.org/diseases-conditions/cervical-cancer/symptoms-causes/syc-20352501)\"\u003e\u003cstrong\u003ewww.mayoclinic.org/diseases-conditions/cervical-cancer/symptoms-causes/syc-20352501)\u003c/strong\u003e\u003c/a\u003e. 2020.\u003c/li\u003e\n\u003cli\u003eZambia Statistics Agency - ZSA, Ministry of Health - MOH, University Teaching Hospital Virology Laboratory - UTH-VL, ICF: \u003cstrong\u003eZambia Demographic and Health Survey 2018\u003c/strong\u003e. 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In\u003cem\u003e.\u003c/em\u003e Dar es Salaam, Tanzania: MoHCDGEC, MoH, NBS, OCGS, and ICF; 2016.\u003c/li\u003e\n\u003cli\u003eYotebieng M, Wenzi LK, Basaki E, Batumbula ML, Tabala M, Mungoyo E, Mangala R, Behets F: \u003cstrong\u003eProvider-Initiated HIV testing and counseling among patients with presumptive tuberculosis in Democratic Republic of Congo\u003c/strong\u003e. \u003cem\u003eThe Pan African Medical Journal \u003c/em\u003e2016, \u003cstrong\u003e25\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eKyaw KWY, Kyaw NTT, Kyi MS, Aye S, Harries AD, Kumar AM, Oo NL, Satyanarayana S, Aung ST: \u003cstrong\u003eHIV testing uptake and HIV positivity among presumptive tuberculosis patients in Mandalay, Myanmar, 2014-2017\u003c/strong\u003e. \u003cem\u003ePloS one \u003c/em\u003e2020, \u003cstrong\u003e15\u003c/strong\u003e(6):e0234429.\u003c/li\u003e\n\u003cli\u003eDelany-Moretlwe S, Bello B, Kinross P, Oliff M, Chersich M, Kleinschmidt I, Rees H: \u003cstrong\u003eHIV prevalence and risk in long-distance truck drivers in South Africa: a national cross-sectional survey\u003c/strong\u003e. \u003cem\u003eInternational journal of STD \u0026amp; AIDS \u003c/em\u003e2014, \u003cstrong\u003e25\u003c/strong\u003e(6):428-438.\u003c/li\u003e\n\u003cli\u003eBot\u0026atilde;o C, Horth RZ, Frank H, Cummings B, Inguane C, Sathane I, McFarland W, Raymond HF, Young PW: \u003cstrong\u003ePrevalence of HIV and associated risk factors among long distance truck drivers in Inchope, Mozambique, 2012\u003c/strong\u003e. \u003cem\u003eAIDS and Behavior \u003c/em\u003e2016, \u003cstrong\u003e20\u003c/strong\u003e(4):811-820.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adult HIV risk assessment tool, Undiagnosed HIV, Never tested for HIV, HIV testing yield","lastPublishedDoi":"10.21203/rs.3.rs-209246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-209246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThere are a number of risk factors being used to identify undiagnosed HIV infected adults. As the number of undiagnosed people gets lesser and lesser, it is important to know if existing risk factors and risk assessment tools are valid for use. In this study, we validate existing HIV risk assessment tools and see if they are worth using for HIV case finding among adults who remain undiagnosed. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The Tanzania and Zambia Population-Based HIV Impact Assessment (PHIA) household surveys were conducted during 2016. We used adult interview and HIV datasets to assess validity of different HIV risk assessment tools. We first included 12 risk factors (being divorced, separated or widowed (DSW); having an HIV+ spouse; having one of the following within 12 months of the survey: paid work, slept away from home for at least a month, had multiple sexual partners, paid for sex, had sexually transmitted infection (STI), being a tuberculosis (TB) suspect, being very sick for at least 3 months; had ever sold sex; diagnosed with cervical cancer; and had TB disease into a risk assessment tool and assessed its validity by comparing it against HIV test result. Sensitivity, specificity and predictive value of the tool were assessed against the HIV test result. A receiver operator characteristic (ROC) analysis was conducted to determine a suitable cut-off score in order to have a tool with better sensitivity, specificity, and PPV. ROC comparison statistics was used to statistically test equality between AUC (area under the curve) of the different scores. ROC comparison statistics was also used to determine which risk assessment tool was better compared to the tool that contained all risk factors. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Of 14,820 study participants, 57.8% were men, and had a median age of 30 (IQR: 21-24). HIV prevalence was 2.3% (95% confidence interval (CI): 2.0-2.6). For the tool containing all risk factors, HIV prevalence was 1.0% when none of the risk factors were positive (Score 0) compared to 3.2% when at least one factor (Score ≥1) was present and 8.0% when ≥4 risk factors were present. Sensitivity, specificity, PPV, and NPV were 82.3% (78.6%-85.9%), 41.9% (41.1%-42.7%), 3.2% (2.8%-3.6%), and 99.0% (98.8%-99.3%), respectively. The use of a tool containing conventional risk factors (all except those related with working and sleeping away) was found to have higher AUC compared to the use of all risk factors (p value \u0026lt;0.001), with corresponding sensitivity, specificity, PPV, and NPV of 63.5% (58.9%-68.1%), 66.2% (65.5%-67.0%), 4.2% (3.6%-4.8%), and 98.7% (98.5%-98.9%), respectively. \u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Use of a screening tool containing conventional risk factors improved HIV testing yield compared to doing universal testing. Prioritizing people who fulfil multiple risk factors should be explored further to improve HIV testing yield.\u003c/p\u003e","manuscriptTitle":"Validation of HIV Risk Screening Tool to Identify Infected Adults and Adolescents \u0026gt;14 years at Community Level","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-05 20:26:20","doi":"10.21203/rs.3.rs-209246/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ce661a2-2549-4f73-a664-55eb52536943","owner":[],"postedDate":"February 5th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2275121,"name":"Health Economics \u0026 Outcomes Research"},{"id":2275122,"name":"Health Policy"},{"id":2275123,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-03-02T02:29:04+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-05 20:26:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-209246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-209246","identity":"rs-209246","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}